Project: Identifying novel subtypes of youth depression and anxiety using machine learning methods – also offered as MBiomedSc

Supervisors: Dr. Lianne Schmaal, Dr. Chris Davey, Dr. Ben HarrisonProject Site: Orygen the National Centre of Excellence in Youth Mental

Health, University of Melbourne Contact: Dr. Lianne Schmaal, T: 0477550490 E: lianne.schmaal@unimelb.edu.au

Project description: Anxiety and depression, together referred to as internalising disorders, are leading causes of disability in young Australians. Efforts to intervene at an early stage of internalising disorders are critical, but are currently hampered by low diagnostic validity and poor specificity of symptom-based classifications of young people with emerging mental disorders. This can in part be explained by the fact that current symptom-based classifications assume that psychiatric disorders are discrete and dissociable entities, and are agnostic about underlying biological mechanisms. There is a clear need for developing an alternative diagnostic framework that can guide clinicians in the treatment of young people in early stages of mental illness and that can identify young people at-risk for a progressive course of internalising symptoms. This project aims to identify novel subtypes of youth depression and anxiety by integrating neurobiological information with clinical and behavioural data using machine learning techniques.

This project (or potentially PhD project) will use functional magnetic resonance imaging (fMRI) and data on symptom dimensions. The student will be involved in acquisition of new neuroimaging and clinical data, processing of neuroimaging data and using advanced statistical methods to identify novel phenotypes of youth depression and anxiety. Candidates with an interest in psychology, biological psychiatry and imaging neuroscience are encouraged to apply. Having prior experience with neuroimaging analyses and having strong statistical and computer programming skills is desirable. Further detail about this project is available upon request.

Project: World-wide data-sharing to detect neurobiological alterations in MDD: the worldwide ENIGMA Major Depressive Disorder consortium– also offered as MBiomedSc

Supervisors: Dr. Lianne Schmaal Project Site: Orygen the National Centre of Excellence in Youth Mental

Health, University of MelbourneContact: Dr. Lianne Schmaal, T: 0477550490, E: lschmaal@unimelb.edu.au

Project description: Major depressive disorder (MDD) is a highly debilitating disorder that has an enormous detrimental impact on patient's life and a high social and economic burden. Many studies have identified structural and functional brain alterations in MDD. However, to date, volumetric and functional brain differences have not always been consistent, which may in part be explained by small sample sizes and differences in methodological and clinical characteristics between studies. To address the limited statistical power of prior studies, the MDD working group within the “Enhancing NeuroImaging Genetics through Meta-Analysis”, or ENIGMA, was initiated a few years ago, see http://enigma.ini.usc.edu/ongoing/enigma-mdd-working-group/

The overall aim of the ENIGMA MDD consortium is to establish the neurobiological correlates underlying variation in disease profile and disease course. Currently, 31 research sites from around the world are participating in ENIGMA MDD and share neuroimaging data from >8,000 healthy controls and >2,500 MDD patients.

The student will support ongoing ENIGMA MDD work, which includes development and execution of data processing, quality assurance and statistical analyses protocols for neuroimaging (structural MRI, resting state fMRI and DTI), organising and harmonising databases, communicating with members of the consortium across the world, writing scientific papers on the above topics, and incorporating the research into a PhD thesis. Candidates with an interest in psychology, biological psychiatry and imaging neuroscience are encouraged to apply. Having prior experience with neuroimaging analyses and having strong statistical and computer programming skills is desirable. Further detail about this project is available upon request.

Project: Neurobiology of Suicide Thoughts and Behaviours in Youth with Mental Disorders– also offered as MBiomedSc

Supervisors: Dr. Lianne Schmaal Project Site: Orygen the National Centre of Excellence in Youth Mental

Health, University of MelbourneContact: Dr. Lianne Schmaal, T: 0477550490, E: lschmaal@unimelb.edu.au

Project description: Globally, suicide is the second most common cause of death for adolescents and young adults. More adolescents die by suicide than from cancer, heart disease, AIDS, birth defects, stroke, pneumonia, influenza, and chronic lung disease. Suicidal thoughts and behaviours (STBs) typically emerge during adolescence, and the incidence of suicide rises sharply from childhood to adolescence (i.e. from 1.2 to 19.2 per 100.000). About 16% of teens think about suicide, and approximately 8% report making an attempt in the past year. To improve preventative intervention treatment for STBs, it is critical to identify neurobiological mechanisms and psychosocial risk factors that confer increased risk. The project aims to elucidate transdiagnostic neurobiological and social mechanisms and trajectories underlying STBs in adolescence, a critical period of development when STBs emerge.

The student will be part of a multidisciplinary international research consortium with extensive expertise in adolescent mental health and STBs, i.e. the Help Overcome and Prevent the Emergence of Suicide (HOPES) consortium. The student will analyse large-scale international datasets from more than 14 countries world-wide and report the results in a scientific paper. The student will help organise and harmonise databases, communicate with members of the consortium across the world. Candidates with an interest in psychology, biological psychiatry, (youth) mental health and imaging neuroscience are encouraged to apply. Having prior experience with neuroimaging analyses and having strong statistical and computer programming skills is desirable. Further detail about this project is available upon request.